AI-Generated Test Case Generators for Complex Code Scenarios
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Solution Overview
Problem
Traditional methods for generating unit test cases are inefficient and have low test coverage, making it difficult to handle complex and changing service scenarios.
Innovation Solution
A method utilizing a large model to generate a test case generator based on test requirement information, which then produces target test cases, improving efficiency and flexibility in test case generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual writing and templates are used for generating unit test cases, then the process is simple to implement, but the generation efficiency is low and test coverage is insufficient
Solution Approach 1:
The patent introduces a large model as an intermediary component between the test requirement information and the test case generator. The large model processes the test requirements and generates a specialized generator, which then produces test cases. This intermediary approach enables automated test case generation with high efficiency while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent segments the test case generation process into distinct modules: obtaining test requirement information, calling the large model to generate a target generator, and using the generator to produce test cases. This segmentation allows each component to be optimized independently, improving overall generation efficiency without overwhelming system complexity.
2Adaptability or versatility
If traditional template-based methods are used, then the implementation is straightforward, but the test coverage cannot meet complex and changing service scenarios
Solution Approach 1:
The patent implements a dynamic test case generation system where the large model and target generator adapt to different test requirements. Instead of static templates, the system dynamically generates generators based on specific test needs, enabling high adaptability to complex and changing service scenarios while maintaining efficient automated generation.
Solution Approach 2:
The system changes the parameters and characteristics of the generator based on the test requirement information processed by the large model. This allows the generator to be customized for different service scenarios, achieving high adaptability while the automated process maintains high productivity.
3Reliability
If automated generation using large models is implemented, then test case generation efficiency and test coverage are improved, but the system complexity increases
Solution Approach 1:
The patent employs a universal large model that can handle multiple types of test requirements and generate different kinds of test cases. This multi-functional approach improves test coverage quality across diverse scenarios while avoiding the need for multiple specialized systems, thereby controlling architecture complexity.
Solution Approach 2:
The system implements self-service through automated processes where the large model automatically analyzes test requirements and generates appropriate test cases without manual intervention. This automation improves test coverage quality while the self-service nature reduces operational complexity despite the sophisticated underlying architecture.
Data Source
AI summary
Provided are a method for generating a test case, an electronic device and a storage medium, relating to the field of data processing technology, and in particular to the fields of artificial intelligence, large model and other technologies. The method includes: obtaining test requirement information of code to be tested; calling the large model based on the test requirement information of the code to be tested to generate a target generator for the code to be tested; and obtaining a plurality of target test cases for testing the code to be tested based on the target generator.